In the Field

State

Activity

Type

Contact

California

Implementation Assistance Program – Awarded to the Metropolitan Transportation Commission (MTC) to implement Partnership to Develop an Integrated, Advanced Travel User Incentive Demand Model and a Fine-Grained, Time-Sensitive Network (C10). The MTC is using the best practices in this bundle in order to represent transit accessibility and passenger behavior at a fine-grained level within their respective activity-based travel demand models. This will help to improve many transit-related projects in the Bay Area and Puget Sound regions. A strong partnership exists between the agency partners as a result of previous project experience.

Implementation Assistance Program – Awarded to the San Diego Association of Governments (SANDAG) to implement Understanding Highway Congestion and Price Affect Travel Demand / Understanding Ops, Tech and Design to Meet Hwy California DOT Capacity Needs (C04/C05). SANDAG will implement best practices from C04 in the Activity-Based Model. The model will be used for important decision making on issues such as managed lanes, tolling, high occupancy, and land-use policy.

Implementation Assistance Program – Awarded to the Atlanta Regional Commission (ARC) to implement Partnership to Develop an Integrated, Advanced Travel User Incentive Demand Model and a Fine-Grained, Time-Sensitive Network (C10). ARC is using the best practices from this bundle to implement a tool that can better address the demand on existing and future dynamically priced toll and high-occupancy toll lanes, and address evacuation planning during inclement weather. Partnerships with the Georgia Department of Transportation, Georgia Regional Transportation Agency, Metropolitan Atlanta Rapid Transit Authority, and Georgia’s State Road and Tollway Authority will be utilized.

Implementation Assistance Program – Awarded to the Maryland State Highway Administration (MSHA) and the Baltimore Metropolitan Council (BMC) to implement Partnership to Develop an Integrated, Advanced Travel User Incentive Demand Model and a Fine-Grained, Time-Sensitive Network (C10). MSHA and BMC are using the best practices from this bundle to better analyze destination choice, mode choice, route choice, and departure time decisions, as well as network impacts on travelers. Partnerships between the University of Maryland and Arizona State University will assist Maryland during this project.

Implementation Assistance Program – Awarded to the Durham-Chapel Hill-Carrboro Metropolitan Planning Organization (DCHC MPO) to implement The Effect of Smart Growth Policies on Travel Demand/SmartGAP (C16). DCHC MPO is adopting the Planning and Assessment Tool for the Triangle Region in North Carolina. This tool will address the issues of auto operating cost charges (especially gas price increases), proposed Transit Oriented Development land use policies, the benefits of smart growth development, and aging of the population.

Implementation Assistance Program – Awarded to the Ohio Department of Transportation (ODOT) to implement Partnership to Develop an Integrated, Advanced Travel User Incentive Demand Model and a Fine-Grained, Time-Sensitive Network (C10). ODOT is using the best practices from this bundle to analyze strategies recommended for Active Transportation and Demand Management in the Cleveland, Columbus, Cincinnati, and Dayton regions.

Implementation Assistance Program – Awarded to the Oregon Department of Transportation (ODOT) to implement The Effect of Smart Growth Policies on Travel Demand/SmartGAP (C16). ODOT is using this tool to analyze how alternating land-use policies could affect vehicle travel, non-motorized travel, and greenhouse gas emissions in the Corvallis area. SmartGAP’s more complete representation to improve scenario analysis will be utilized.

Implementation Assistance Program – Awarded to the Delaware Valley Regional Planning Commission (DVRPC) to implement The Effect of Smart Growth Policies on Travel Demand/SmartGAP (C16). DVRPC is using the Planning Policy and Assessment Tool in order to further their ability to quickly and inexpensively evaluate future growth scenarios. This tool would also be used to prescreen policy scenarios before undertaking an extensive travel demand model and exercises that are resource-intensive.

Challenge

Existing transportation planning models deal with average conditions and treat travel
as a series of sequential decisions. As such, existing models have difficulty addressing
a number of areas including:

Addressing these areas requires greater spatial and temporal detail than is typically
available with the traditional static, sequential approach. Decision makers also
need a better understanding of the impact of operational improvements on highway
capacity and reliability, as well as the effects of congestion, reliability, and
pricing on travel demand.

Solution

The Bundle: TravelWorks (C10/C04/C05/C16)

The TravelWorks bundle for integrated travel demand
modeling aims to encourage more agencies to use advanced travel analysis tools and
to support agencies leading the way in using these tools. These products were bundled
together because of their technical and subject matter commonalities, as a result
of an executive review committee consisting of American Association of State Highway
and Transportation Officials (AASHTO) and Federal Highway Administration (FHWA)
leaders.

The SHRP2 TravelWorks bundle provides approaches
for developing integrated travel analysis models-that include traveler decision
inputs-to better align the outcomes with real-world conditions. Frequent constraints
for metropolitan travel forecasting models include limited feedback between supply
and demand sides, limited inputs on traveler behavior related to pricing and congestion,
and limited use and guidance about activity-based models and dynamic traffic assignment.
Many transportation agencies already use some of these tools and tactics in their
current business practices.

FHWA, AASHTO, and the Transportation Research Board (TRB) also promote various strategies
and tactics through their respective programs and initiatives. To evaluate the success
of the implementation effort, the implementation plan for this product will offer
examples of performance measures that can be used to track progress and outcomes
in making the TravelWorks a nationally recognized,
well-used, and effective resource.

Successful implementation of the TravelWorks bundle will involve a wide range of stakeholders-from TRB’s research experience and
in-depth knowledge of the SHRP2 products-to FHWA and AASHTO’s leadership of
implementation activities-to practitioners and decision makers in the public,
academic, and private sectors.

The Integrated Dynamic Travel Model (C10) links travel behavior choices, such as
departure time or route, with congested network conditions to better reflect real-world
dynamics in the model. Planners can then more directly test the effects of various
alternatives on congestion. This SHRP2 Solution advances existing modeling applications
to include sensitivities for traffic shifts by time of day or route in response
to capacity increases, operation actions, or management actions. It can also dynamically
integrate travel-time reliability, greenhouse gas emissions, road pricing, mode
shifts, and non-travel choices such as work/shop at home or flextime policies. The
immediate benefit of the models is that they address the essential question of how
travel behavior responds to network conditions and network conditions respond to
behavior. The result is a dynamic model that better analyzes transportation alternatives
and provides more precision to support transportation planning decisions. Ultimately,
the benefit of more effective and precise modeling is more informed transportation
planning.

Travel demand modeling systems can now reflect how travelers respond to congestion,
travel time reliability, and pricing, so that decisions about operational improvements
can be based on more realistic models. With better models, agencies better understand
how operations projects can improve the function of their highway networks. This
SHRP2 product provides mathematical descriptions of the full range of highway user
behavioral responses to congestion, travel time reliability, and pricing; formatted
for input to current and developing travel demand models.

With the enhanced capability to measure the cost and effectiveness of traffic operations
strategies, planners and decision makers can demonstrate whether a strategy solves
a particular congestion problem and can more confidently act to improve the function
of their highway networks. This SHRP2 product is a guide for modelers on how to
compare the effectiveness of less complex operational strategies, such as intersection
channelization, with more expensive and complex treatments, such as adding general
purpose highway lanes.

The Effect of Smart Growth Policies on Travel Demand (C16)

This SHRP2 product provides planners with a rapid scenario assessment tool that
allow them to estimate impacts of changes to the built environment, travel demand,
and transportation supply and demand management policies on peak-hour transportation,
as well as its effects on sprawl, energy reduction, active travel, and carbon footprints.
The predictive tool allows a user to test different scenarios for land use, population
growth, and transportation strategies, and provides ‘sketch’ – or high level estimates
of system usage and regional accessibility across multiple transport modes, accidents
by severity, fuel consumption and emissions, and peak hour operating conditions.

Benefits

SHRP2 has developed this linkage to better reflect behavior in the models so planners
can more directly test the effects of various alternatives on congestion. Transportation
agencies will be able to estimate travel demand in a way that integrates activities,
networks, and the environment. These advanced models are sensitive to the reciprocal
interplay of traveler behavior and transportation network conditions, including
mode choice options. The models support more informed decisions about adding highway
and transit capacity, enhancing traffic operations, introducing priced roads, and
improving traveler information. An easy-to-use sketch planning tool for assessing
land use/transportation interactions is also available.

The Integrated Dynamic Travel Model (C10) links travel behavior choices, such as
departure time or route, with congested network conditions to better reflect real-world
dynamics in the model. Planners can then more directly test the effects of various
alternatives on congestion. This SHRP2 Solution advances existing modeling applications
to include sensitivities for traffic shifts by time of day or route in response
to capacity increases, operation actions, or management actions. It can also dynamically
integrate travel-time reliability, greenhouse gas emissions, road pricing, mode
shifts, and non-travel choices such as work/shop at home or flextime policies. The
immediate benefit of the models is that they address the essential question of how
travel behavior responds to network conditions and network conditions respond to
behavior. The result is a dynamic model that better analyzes transportation alternatives
and provides more precision to support transportation planning decisions. Ultimately,
the benefit of more effective and precise modeling is more informed transportation
planning.

Travel demand modeling systems can now reflect how travelers respond to congestion,
travel time reliability, and pricing, so that decisions about operational improvements
can be based on more realistic models. With better models, agencies better understand
how operations projects can improve the function of their highway networks. This
SHRP2 product provides mathematical descriptions of the full range of highway user
behavioral responses to congestion, travel time reliability, and pricing; formatted
for input to current and developing travel demand models.

With the enhanced capability to measure the cost and effectiveness of traffic operations
strategies, planners and decision makers can demonstrate whether a strategy solves
a particular congestion problem and can more confidently act to improve the function
of their highway networks. This SHRP2 product is a guide for modelers on how to
compare the effectiveness of less complex operational strategies, such as intersection
channelization, with more expensive and complex treatments, such as adding general
purpose highway lanes.

The Effect of Smart Growth Policies on Travel Demand (C16)

This SHRP2 product provides planners with a rapid scenario assessment tool that
allow them to estimate impacts of changes to the built environment, travel demand,
and transportation supply and demand management policies on peak-hour transportation,
as well as its effects on sprawl, energy reduction, active travel, and carbon footprints.
The predictive tool allows a user to test different scenarios for land use, population
growth, and transportation strategies, and provides ‘sketch’ – or high level estimates
of system usage and regional accessibility across multiple transport modes, accidents
by severity, fuel consumption and emissions, and peak hour operating conditions.